Tomorrow I start Joe Hudson’s Connection course. This week, I’ve started reading Anne-Laure Le Cunff’s Tiny Experiments. Each evening, I’m working with apps to sharpen my algorithmic thinking. Somehow these three seemingly separate things feel deeply connected – the science of small experiments, the art of human connection, the systematic way of approaching the world with curiosity.
All mindset stuff, really. How to approach the world with wonder and curiosity.
This got me thinking about a conversation I had recently with someone who’d just graduated high school. They wanted to become a developer but were struggling with the maths in their computer science degree. “I’m just not a maths person,” they said, with that familiar resignation I recognised in myself not so long ago.
But what I’ve come to realise is that the maths isn’t separate from the thinking. It’s all one integrated way of approaching problems with curiosity and systematic experimentation.
I used to think I was no good at maths. There’s this huge global preconception that maths is hard, or worse, boring. I also used to think all the sciences had their own containers and domains. Engineering over here, computer science over there, biology somewhere else entirely.
But algorithms are everywhere.
When you’re developing a bread recipe, testing different hydration levels and fermentation times, noting which combinations create the perfect crumb – that’s scientific method. That’s data collection. That’s algorithmic thinking. Cooking, especially baking, is all about chemistry, but we don’t typically call bakers chemists.
When you’re planning which plants to grow together in your garden, observing which companions thrive and which compete, adjusting based on soil conditions and seasonal patterns – that’s A/B testing. That’s experimental design. That’s pattern recognition.
I’m currently really enjoying Algorithms to Live By, and there’s this bit about apartment hunting. The book explains that harried renters need the 37% rule: spend 37% of your search time exploring options without committing, then immediately commit to the first place that beats whatever you’ve already seen. It’s not just intuitive compromise between looking and leaping – it’s the provably optimal solution to what computer scientists call “optimal stopping” problems.
Once I started looking, I could see it everywhere. The explore/exploit trade-off that helps me balance trying new things with enjoying my favourites. Sorting theory for organising my desk. Caching theory for arranging my wardrobe. Scheduling theory for managing my time. Brian Griffiths makes some excellent points, algorithms really are everywhere.
This made me wonder about engineering as a science. I’ve always thought of it as one, but why is it a whole separate department at universities? Why do we compartmentalise education into these distinct subjects when the underlying thinking is all connected?
Some European school systems already get this. The Danish system integrates various aspects of science across different subjects rather than separating them into distinct disciplines. Programs like Cambridge IGCSE offer Combined Science qualifications where students study biology, chemistry, and physics as one interconnected course. At the university level, interdisciplinary Bachelor’s programs provide broad education that integrates multiple core scientific disciplines.
There’s an urge to recombine, to see the patterns.
Following scientific method – even loosely, even personally – leads to better understanding of everything. Using analytics and data to drive decisions creates better products, tools, more efficient use of time. When we debug code, we’re hypothesising about what might be wrong, testing our theories, collecting evidence. When we iterate on user interfaces, we’re running experiments and measuring outcomes.
It’s all the same thing in different forms.
Everything is connected, and once your mind starts thinking in this way, it’s the same underlying pattern recognition showing up across every domain. The wonder of discovering how interconnected everything really is has changed how I approach technical problems, as well as how I mentor others.
When someone tells me they’re “not a maths person,” I can say with confidence: “Actually, you might already have these thinking skills – let me show you where.” Because they’re probably already experimenting in their kitchen, optimising their commute, troubleshooting why their plants aren’t thriving, finding patterns in their work that help them make better decisions.
That’s scientific thinking. That’s algorithmic reasoning. That’s the same cognitive toolkit that powers everything from machine learning to sourdough starters.
So here’s what I’m wondering.. what other “separate” skills are actually expressions of this same underlying pattern recognition? Where are we all already collecting data and running experiments in our daily life without realising it?
Maybe it’s time we stopped teaching subjects in isolation and started teaching the thinking that connects them all – the curiosity, the systematic experimentation, the willingness to test assumptions and adjust based on evidence.
Because the truth is, we’re all scientists. We’re all running tiny experiments, all the time. We just don’t always recognise the algorithm behind the wonder.
